A Survey on Personalized Alignment -- The Missing Piece for Large Language Models in Real-World Applications
Large Language Models (LLMs) have demonstrated remarkable capabilities, yet their transition to real-world applications reveals a critical limitation: the inability to adapt to individual preferences while maintaining alignment with universal human values. Current alignment techniques adopt a one-size-fits-all approach that fails to accommodate users' diverse backgrounds and needs. This paper presents the first comprehensive survey of personalized alignment-a paradigm that enables LLMs to adapt their behavior within ethical boundaries based on individual preferences. We propose a unified framework comprising preference memory management, personalized generation, and feedback-based alignment, systematically analyzing implementation approaches and evaluating their effectiveness across various scenarios. By examining current techniques, potential risks, and future challenges, this survey provides a structured foundation for developing more adaptable and ethically-aligned LLMs.
Code (1)
Tasks
ManagementSurveyMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
A Survey on Personalized and Pluralistic Preference Alignment in Large Language Models
Personalized preference alignment for large language models (LLMs), the process of tailoring LLMs to individual users' preferences, is an emerging research direction spanning the area of NLP and personalization. In this …
Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing Borders
This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with eroded or missing borders. Missing borders is a common real-world situation, for example, when dealing with the reconstruction of broken artif…
A Survey of Personalized Large Language Models: Progress and Future Directions
Large Language Models (LLMs) excel in handling general knowledge tasks, yet they struggle with user-specific personalization, such as understanding individual emotions, writing styles, and preferences. Personalized Large…
Emotion RecognitionGeneral KnowledgeRecommendation SystemsSurveyPersonalized Pieces: Efficient Personalized Large Language Models through Collaborative Efforts
Personalized large language models (LLMs) aim to tailor interactions, content, and recommendations to individual user preferences. While parameter-efficient fine-tuning (PEFT) methods excel in performance and generalizat…
parameter-efficient fine-tuningFair-PP: A Synthetic Dataset for Aligning LLM with Personalized Preferences of Social Equity
Human preference plays a crucial role in the refinement of large language models (LLMs). However, collecting human preference feedback is costly and most existing datasets neglect the correlation between personalization …
Survey